This article presents the state of the field of large-area tactile sensing in robotics and prosthetics, particularly focusing on neural-like tactile data handling, energy autonomy, and advanced manufacturing based on printed electronics.

This paper presents graphene field-effect transistor (GFET) based pressure sensors for tactile sensing. The sensing device comprises GFET connected with a piezoelectric metal-insulator-metal (MIM) capacitor in an extended gate configuration. The application of pressure on MIM generates a piezo-potential which modulates the channel current of GFET. The fabricated pressure sensor was tested over a range of 23.54-94.18 kPa, and it exhibits a sensitivity of 4.55 Â 10 À3 kPa À1. Further, the low voltage ($100 mV) operation of the presented pressure sensors makes them ideal for wearable electronic applications. V

Memristive devices based on vertical heterostructures of graphene and TiOx show a significant power reduction that is up to ∼10(3) times smaller than that of conventional structures. This power reduction arises as a result of a tunneling barrier at the interface. The barrier is tunable, opening up the possibility of engineering several key memory characteristics.

Two-dimensional (2D) materials have great potential in photonic and optoelectronic devices. However, the relatively weak light absorption in 2D materials hinders their application in practical devices. Here, we propose a general approach to achieve angle-selective perfect light absorption in 2D materials. As a demonstration of the concept, we experimentally show giant light absorption by placing large-area single-layer graphene on a structure consisting of a chalcogenide layer atop a mirror and achieving a total absorption of 77.6% in the mid-infrared wavelength range (~13 μm), where the graphene contributes a record-high 47.2% absorptivity of mid-infrared light. Construction of such an angle-selective thin optical element is important for solar and thermal energy harvesting, photo-detection and sensing applications. Our study points to a new opportunity to combine 2D materials with photonic structures to enable novel device applications.

An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body–like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system–like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.

Graphene has great potential for high-performance flexible electronics. Although studied for more than a decade, contacting graphene efficiently, especially for large-area, flexible electronics, is still a challenge. Here, by engineering the graphene-metal van der Waals (vdW) contact, we demonstrate that ultra-low contact resistance is achievable via a bottom-contact

No abstract

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin–like or peripheral nervous system–like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.